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Title Classification of Melanoma Presence and Thickness Based on Computational Image Analysis
Authors SÁNCHEZ MONEDERO, JAVIER, Saez, Aurora , PÉREZ ORTIZ, MARÍA, Antonio Gutierrez, Pedro , Hervas-Martinez, Cesar
External publication No
Means Lect. Notes Comput. Sci.
Scope Proceedings Paper
Nature Científica
JCR Quartile 4
SJR Quartile 2
SJR Impact 0.339
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964047348&doi=10.1007%2f978-3-319-32034-2_36&partnerID=40&md5=b261632185eccb4789dc32a8ad8f5764
Publication date 01/01/2016
ISI 000389499600036
Scopus Id 2-s2.0-84964047348
DOI 10.1007/978-3-319-32034-2_36
Abstract Melanoma is a type of cancer that occurs on the skin. Only in the US, 50,000-100,000 patients are yearly diagnosed with melanoma. Five year survival rate highly depends on early detection, varying between 99% and 15% depending on the melanoma stage. Melanoma is typically identified with a visual inspection and lately confirmed and classified by a biopsy. In this work, we propose a hybrid system combining features which describe melanoma images together with machine learning models that learn to distinguish melanoma lesions. Although previous works distinguish melanoma and non-melanoma images, those works focus only in the binary case. Opposed to this, we propose to consider finer classification levels within a five class learning problem. We evaluate the performance of several nominal and ordinal classifiers using four performance metrics to provide highlights of several aspects of classification performance, achieving promising results.
Keywords Melanoma; Feature extraction; Dermoscopic image; Computer vision; Machine learning; Multi-class; Ordinal classification; Imbalanced classification
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